Installation

pip install telemem            # core (text memory)
pip install "telemem[mcp]"     # + MCP server
pip install "telemem[video]"   # + video/multimodal pipeline
pip install "telemem[all]"     # everything

Install from source (editable), with the extras you need

pip install -e ".[all]" ```

Quick Start

Example

Set your OpenAI API key:

export OPENAI_API_KEY="your-openai-api-key"

```python

python examples/quickstart.py

import telemem as mem0

memory = mem0.Memory()

messages = [ {"role": "user", "content": "Jordan, did you take the subway to work again today?"}, {"role": "assistant", "content": "Yes, James. The subway is much faster than driving. I leave at 7 o'clock and it's just not crowded."}, {"role": "user", "content": "Jordan, I want to try taking the subway too. Can you tell me which station is closest?"}, {"role": "assistant", "content": "Of course, James. You take Line 2 to Civic Center Station, exit from Exit A, and walk 5 minutes to the company."} ]

memory.add(messages=messages, user_id="Jordan") results = memory.search("What transportation did Jordan use to go to work today?", user_id="Jordan") for hit in results["results"]: # same result shape as mem0 print(hit["memory"])

`Memory()` uses the default provider settings inherited from `mem0ai`. To use the repository's local Qwen + FAISS configuration, load `config/config.yaml` explicitly:

config = load_config("config/config.yaml") memory = mem0.Memory(config=config)

The runnable examples also honor the same configuration through `TELEMEM_CONFIG`:

Multimodal Example

Run the multimodal demo:

python examples/quickstart_mm.py

On the first run, frames, captions and VDB JSON will be generated under the chosen output_dir. The repository ships a small sample video; generating captions and the video database still requires configured VLM and embedding services unless you already have these artifacts locally.

Complete code example:

```python import telemem as mem0 from pathlib import Path from telemem.mm_utils.core import extract_choice_from_msg

📄 Metadata Example (_meta.json)

{
 "summary": "Characters discussed the upcoming action plan.",
 "sample_id": "session_001",
 "round_index": 3,
 "timestamp": "2024-01-01T00:00:00Z",
 "user": "Jordan" // Only present in person_*.json
}

All memories include summary, round number, timestamp, and character, facilitating auditing and debugging.

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Experimental Configuration

• LLM: Qwen3-8B (thinking mode disabled)
• Embedding model: Qwen3-Embedding-8B
• Metric: QA accuracy

| Method | Overall(%) | |:--------------------------------------------------------- |:---------- | | RAG | 62.45 | | _Mem0 | 70.20 | | MOOM | 72.60 | | A-mem | 73.78 | | Memobase | 76.78 | | TeleMem | 86.33 |

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Development Environment

Using uv (recommended — creates .venv from the committed uv.lock for a reproducible environment):

uv sync --all-extras   # install TeleMem (editable) + all extras, incl. MCP
uv run python examples/quickstart.py

Or with conda + pip:

```shell

Create and activate virtual environment

conda create -n telemem python=3.10 conda activate telemem

Multimodal Extensions

Beyond text memory, TeleMem further extends multimodal capabilities. Drawing inspiration from Deep Video Discovery's Agentic Search and Tool Use approach, we implemented two core methods in the TeleMemory class to support intelligent storage and semantic retrieval of video content.

Framework Integrations

TeleMem drops into any agent framework with the same two calls — search() before answering, add() after each exchange:

Because TeleMem is mem0 API-compatible, any framework adapter written for Mem0's OSS client also works — point it at telemem.Memory instead.

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TeleMem vs Mem0: Core Advantages

TeleMem deeply refactors Mem0 to address characterization, long-term memory, and high performance. Key differences:

| Multimodal Capability | Single image to text only | ✅ **Video Multimodal Memory**: Full video processing pipeline + ReAct multi-step reasoning QA |